Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval
Abhra Chaudhuri (University of Exeter),* Massimiliano Mancini (University of Tübingen), Yanbei Chen (University of Tübingen), Zeynep Akata (University of Tübingen), Anjan Dutta (University of Surrey)
The 33rd British Machine Vision Conference
Abstract
Representation learning for sketch-based image retrieval has mostly been tackled by learning embeddings that discard modality-specific information. As instances from different modalities can often provide complementary information describing the underlying concept, we propose a cross-attention framework for Vision Transformers (XModalViT) that fuses modality-specific information instead of discarding them. Our framework first maps paired datapoints from the individual photo and sketch modalities to fused representations that unify information from both modalities. We then decouple the input space of the aforementioned modality fusion network into independent encoders of the individual modalities via contrastive and relational cross-modal knowledge distillation. Such encoders can then be applied to downstream tasks like cross-modal retrieval. We demonstrate the expressive capacity of the learned representations by performing a wide range of experiments and achieving state-of-the-art results on three fine-grained sketch-based image retrieval benchmarks: Shoe-V2, Chair-V2 and Sketchy. Implementation is available at https://github.com/abhrac/xmodal-vit.Video
Citation
@inproceedings{Chaudhuri_2022_BMVC,
author = {Abhra Chaudhuri and Massimiliano Mancini and Yanbei Chen and Zeynep Akata and Anjan Dutta},
title = {Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0499.pdf}
}
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